Fire source localization is a crucial task in building fire emergencies. Currently, incident commanders rely on reports from firefighters or on-site witnesses to locate the fire source, which can be dangerous and time-consuming. The study proposes an inverse modeling approach for fire source localization using machine learning. The approach builds a model to map the relationship between the fire source location and on-site temperature sensor measurements. The model is trained on simulated fire temperature data, and can be used to localize the fire source in real-time based on temperature data collected from stationary sensors or portable temperature measuring devices. The effectiveness of the proposed approach is demonstrated in a case study of a fire in an actual building floor.
In the ever-evolving world of medicine, artificial intelligence (AI) is rapidly gaining ground as a powerful tool for improving patient care. One of the most promising applications of AI in medicine is in the field of medical imaging, where AI algorithms can analyze medical images, such as X-rays, CT scans, and MRIs, to detect abnormalities […]
A new robotic system developed by researchers at the University of Southern California (USC) could help stroke survivors overcome arm paralysis and improve their mobility. The system uses a combination of robotics and machine learning to assess a patient’s arm use and provide personalized rehabilitation exercises. Stroke is a leading cause of disability worldwide, and […]
Recent advancements in artificial intelligence and machine learning have yielded a formula enabling the accurate prediction of monster waves. This breakthrough is a significant stride in maritime safety, as it enhances our ability to anticipate and mitigate the impact of these formidable natural phenomena. The formula, derived through rigorous computational analyses of historical wave data, […]